| Challenge: | Existing methods to generate character-level features with neural architectures such as CNN or Recurrent Neural Network (RNN) are slow and generate position-independent features. |
| Approach: | They propose a method that uses a densely connected network to extract character-level features from words using CNN and RNN. |
| Outcome: | The proposed method shows robustness and effectiveness while being faster than CNN- or RNN-based methods. |
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| Challenge: | Character-level features are used in many natural language processing algorithms but little is known about the character-level patterns they learn. |
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LemmaTag: Jointly Tagging and Lemmatizing for Morphologically Rich Languages with BRNNs (D18-1)
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| Challenge: | We compare morphologically rich languages with analytical languages like English due to the large vocabulary size and data sparsity. |
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Zero-Shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens (N18-1)
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A Neural Network Model for Part-Of-Speech Tagging of Social Media Texts (L18-1)
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| Challenge: | Recent approaches based on end-to-end Deep Neural Networks (DNNs) have shown promising results for Natural Language Processing (NLP). |
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The Role of Natural Language Processing Tasks in Automatic Literary Character Network Construction (2025.coling-main)
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Learning Better Internal Structure of Words for Sequence Labeling (D18-1)
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| Challenge: | a gap exists between methods for learning representations of sentences and words . authors propose a convolutional neural architecture with no down-sampling for learning words based on character embeddings . |
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A Novel Table-to-Graph Generation Approach for Document-Level Joint Entity and Relation Extraction (2023.acl-long)
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| Challenge: | Existing document-level relation extraction methods assume entities and their mentions are given beforehand, which is inadequate for real-world applications. |
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A Deep Neural Network based Approach for Entity Extraction in Code-Mixed Indian Social Media Text (L18-1)
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Better Feature Integration for Named Entity Recognition (2021.naacl-main)
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| Challenge: | Existing approaches to named entity recognition (NER) focus on stacking the LSTM and graph neural networks (GCNs) however, the exact interaction mechanism between the two types of features is not clear and the performance gain is not significant. |
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Neural Adaptation Layers for Cross-domain Named Entity Recognition (D18-1)
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| Challenge: | Named entity recognition is a type of information extraction task whereby features can be designed based on domain-specific knowledge. |
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